This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features.
Testing the association between 18194 genes and 4 clinical features across 155 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.
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2 genes correlated to 'Time to Death'.
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CHCHD7|79145 , RNASEL|6041
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11 genes correlated to 'AGE'.
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ZNF33B|7582 , C9ORF96|169436 , DYNC1I2|1781 , MRPS31|10240 , LOC100190939|100190939 , ...
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23 genes correlated to 'GENDER'.
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CYORF15B|84663 , CYORF15A|246126 , NCRNA00183|554203 , HDHD1A|8226 , MTMR4|9110 , ...
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No genes correlated to 'RACE'
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=2 | shorter survival | N=1 | longer survival | N=1 |
AGE | Spearman correlation test | N=11 | older | N=1 | younger | N=10 |
GENDER | Wilcoxon test | N=23 | male | N=23 | female | N=0 |
RACE | Kruskal-Wallis test | N=0 |
Time to Death | Duration (Months) | 0.1-175 (median=18.4) |
censored | N = 103 | |
death | N = 51 | |
Significant markers | N = 2 | |
associated with shorter survival | 1 | |
associated with longer survival | 1 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
CHCHD7|79145 | 1.88 | 3.264e-06 | 0.059 | 0.659 |
RNASEL|6041 | 0.58 | 1.138e-05 | 0.21 | 0.337 |
AGE | Mean (SD) | 61.22 (13) |
Significant markers | N = 11 | |
pos. correlated | 1 | |
neg. correlated | 10 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
ZNF33B|7582 | -0.3738 | 1.664e-06 | 0.0303 |
C9ORF96|169436 | -0.363 | 3.446e-06 | 0.0627 |
DYNC1I2|1781 | -0.3563 | 5.364e-06 | 0.0976 |
MRPS31|10240 | -0.351 | 7.557e-06 | 0.137 |
LOC100190939|100190939 | -0.3481 | 9.063e-06 | 0.165 |
ZNF785|146540 | -0.3455 | 1.066e-05 | 0.194 |
ZNF154|7710 | -0.3444 | 1.142e-05 | 0.208 |
MBD5|55777 | -0.3433 | 1.228e-05 | 0.223 |
PSPC1|55269 | -0.3423 | 1.303e-05 | 0.237 |
ANKRD1|27063 | 0.3687 | 1.359e-05 | 0.247 |
GENDER | Labels | N |
FEMALE | 88 | |
MALE | 67 | |
Significant markers | N = 23 | |
Higher in MALE | 23 | |
Higher in FEMALE | 0 |
W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC | |
---|---|---|---|---|
CYORF15B|84663 | 1383 | 2.994e-11 | 5.44e-07 | 0.9829 |
CYORF15A|246126 | 1122 | 7.97e-10 | 1.45e-05 | 0.9851 |
NCRNA00183|554203 | 1400 | 2.276e-08 | 0.000414 | 0.7626 |
HDHD1A|8226 | 1479 | 1.131e-07 | 0.00206 | 0.7492 |
MTMR4|9110 | 1559 | 5.296e-07 | 0.00962 | 0.7356 |
DBF4B|80174 | 1600 | 1.132e-06 | 0.0206 | 0.7286 |
C6ORF147|387097 | 1491 | 1.134e-06 | 0.0206 | 0.7322 |
PRPSAP1|5635 | 1619 | 1.598e-06 | 0.029 | 0.7254 |
CBX1|10951 | 1655 | 3.034e-06 | 0.0551 | 0.7193 |
CBX2|84733 | 1667 | 3.743e-06 | 0.068 | 0.7173 |
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Expresson data file = SARC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = SARC-TP.merged_data.txt
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Number of patients = 155
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Number of genes = 18194
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Number of clinical features = 4
For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels
For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R
For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R
For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R
For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.